Hybrid cloud migration delay risk prediction engine

Abstract

A cloud migration tool manages and monitors a cloud migration project that migrates data from a legacy environment to a target data center environment. The cloud migration tool includes an analytics engine that applies data regression models to generate a delay risk prediction for activities that are scheduled during the cloud migration project.

What is claimed is: 
     
       1. A system comprising:
 network interface circuitry configured to: 
 receive activity data for a cloud migration project; and 
 receive historical activity data for previously completed cloud migration projects; and 
 an analytics engine configured to: 
 group data assigned for migration in bundle units according to bundling rules, the bundling rules comprising client input parameters and historical bundling rules, and assign the bundle units to a cloud migration wave according to wave assignment rules, the wave assignment rules comprising client input parameters and historical wave assignment rules; 
 sequence activities included in the activity data according to a respective activity deadline; 
 determine buffer times between subsequent activities in the activity data; 
 determine delays for activities in the activity data; 
 determine a view value for activities in the activity data based on a corresponding time to data wave migration; 
 determine an activity value for activities in the activity data based on a corresponding activity status; 
 determine a predicted delay of the cloud migration project based on at least a first iteration and a second iteration of a random forest model on the historical activity data for previously completed cloud migration projects, wherein the first iteration selects an optimum number of data trees in the random forest model, and the second iteration fine tunes model arguments in the random forest model; and 
 determine a predicted delay score based on a weighted consideration of the predicted delay, the view value, and the activity value. 
 
     
     
       2. The system of  claim 1 , wherein the predicted delay is weighted greater than the view value or the activity value in the predicted delay score, and wherein the weighted consideration of the predicted delay is adjusted in association with a subsequent iteration of the random forest model. 
     
     
       3. The system of  claim 1 , further comprising configuration circuitry configured to:
 format the activity data into a data format acceptable by the analytics engine. 
 
     
     
       4. The system of  claim 1 , wherein the analytics engine is further configured to consider historical activity issues related to delays included in the historical activity data when the analytics engine determines the predicted delay. 
     
     
       5. The system of  claim 1 , further comprising visualization circuitry configured to:
 generate a graphical user interface (GUI) including the predicted delay score, wherein the predicted delay score is displayed on the GUI according to a color coded scheme corresponding predetermined ranges of predicted delay scores to a distinguishable color. 
 
     
     
       6. The system of  claim 5 , wherein the visualization circuitry is further configured to:
 detect a selection of a displayed activity on the GUI; and 
 display a list of one or more recommended actions for reducing or eliminating a predicted delay of the selected activity. 
 
     
     
       7. The system of  claim 5 , wherein the visualization circuitry is further configured to:
 detect a selection of a displayed cloud migration wave; and 
 display the bundle units assigned to the selected cloud migration wave in a sequence of scheduled migration. 
 
     
     
       8. The system of  claim 1 , wherein the activities are sequenced in each respective bundle according to the respective activity deadline. 
     
     
       9. The system of  claim 8 , wherein the buffer times are determined between subsequent activities in the activity data of each respective bundle, except for a last activity in each respective bundle. 
     
     
       10. The system of  claim 8 , wherein the historical wave assignment rules are applied to respective bundles within the migration wave to arrange a sequence for implementation of migration activities corresponding to the respective bundles. 
     
     
       11. A method comprising:
 storing activity data for a cloud migration project in a wave report database; 
 storing historical activity data for previously completed cloud migration projects in an all activities database; 
 grouping data assigned for migration in bundle units according to bundling rules, the bundling rules comprising client input parameters and historical bundling rules; 
 assigning the bundle units to a cloud migration wave according to wave assignment rules, the wave assignment rules comprising client input parameters and historical wave assignment rules; 
 sequencing, by an analytics engine, activities included in the activity data according to a respective activity deadline; 
 determining, by the analytics engine, buffer times between subsequent activities in the activity data; 
 determining, by the analytics engine, delays for activities in the activity data; 
 determining, by the analytics engine, a view value for activities in the activity data based on a corresponding time to data wave migration; 
 determining, by the analytics engine, an activity value for activities in the activity data based on a corresponding activity status; 
 determining, by the analytics engine, a predicted delay of the cloud migration project based on at least a first iteration and a second iteration of a random forest model on the historical activity data for previously completed cloud migration projects, wherein the first iteration selects an optimum number of data trees in the random forest model, and the second iteration fine tunes model arguments in the random forest model; and 
 determining, by the analytics engine, a predicted delay score based on a weighted consideration of the predicted delay, the view value, and the activity value. 
 
     
     
       12. The method of  claim 11 , wherein the predicted delay is weighted greater than the view value or the activity value in the predicted delay score, and the method further comprises adjusting the weighted consideration of the predicted delay in association with a subsequent iteration of the random forest model. 
     
     
       13. The method of  claim 11 , further comprising:
 formatting, by configuration circuitry, the activity data into a data format acceptable by the analytics engine. 
 
     
     
       14. The method of  claim 11 , wherein determining the predicted delay further comprises considering historical activity issues related to delays included in the historical activity data when the analytics engine determines the predicted delay. 
     
     
       15. The method of  claim 11 , further comprising:
 generating, by visualization circuitry, a graphical user interface (GUI) including the predicted delay score, wherein the predicted delay score is displayed on the GUI according to a color coded scheme corresponding predetermined ranges of predicted delay scores to a distinguishable color. 
 
     
     
       16. The method of  claim 15 , further comprising:
 detecting, by the visualization circuitry, a selection of a displayed activity on the GUI; and 
 displaying, by the visualization circuitry, a list of one or more recommended actions for reducing or eliminating a predicted delay of the selected activity. 
 
     
     
       17. The method of  claim 15 , further comprising:
 detecting, by the visualization circuitry, a selection of a displayed cloud migration wave; and 
 displaying, by the visualization circuitry, the bundle units assigned to the selected cloud migration wave in a sequence of scheduled migration. 
 
     
     
       18. A product comprising:
 a machine-readable medium other than a transitory signal; and 
 instructions stored on the machine-readable medium, the instructions configured to, when executed, cause a machine to: 
 at network interface circuitry: 
 receive activity data for a cloud migration project; and 
 receive historical activity data for previously completed cloud migration projects; and 
 at an analytics engine: 
 group data assigned for migration in bundle units according to bundling rules, the bundling rules comprising client input parameters and historical bundling rules; 
 assign the bundle units to a cloud migration wave according to wave assignment rules, the wave assignment rules comprising client input parameters and historical wave assignment rules; 
 sequence activities included in the activity data according to a respective activity deadline; 
 determine buffer times between subsequent activities in the activity data; 
 determine delays for activities in the activity data; 
 determine a view value for activities in the activity data based on a corresponding time to data wave migration; 
 determine an activity value for activities in the activity data based on a corresponding activity status; 
 determine a predicted delay of the cloud migration project based on at least a first iteration and a second iteration of a random forest model on the historical activity data for previously completed cloud migration projects, wherein the first iteration selects an optimum number of data trees in the random forest model, and the second iteration fine tunes model arguments in the random forest model; and 
 determine a predicted delay score based on a weighted consideration of the predicted delay, the view value, and the activity value. 
 
     
     
       19. The product of  claim 18 , wherein the predicted delay is weighted greater than the view value or the activity value in the predicted delay score, and the method further comprises adjusting the weighted consideration of the predicted delay in association with a subsequent iteration of the random forest model. 
     
     
       20. The product of  claim 18 , where the instructions are further configured to cause the machine to cause the machine to:
 at configuration circuitry: 
 format the activity data into a data format acceptable by the analytics engine. 
 
     
     
       21. The product of  claim 18 , where the instructions are further configured to cause the machine to cause the machine to:
 at the analytics engine: 
 consider historical activity issues related to delays included in the historical activity data when the analytics engine determines the predicted delay. 
 
     
     
       22. The product of  claim 18 , where the instructions are further configured to cause the machine to:
 at visualization circuitry: 
 generate a graphical user interface (GUI) including the predicted delay score, 
 detect a selection of a displayed cloud migration wave, and 
 display the bundle units assigned to the selected cloud migration wave in a sequence of scheduled migration.